Link Prediction Lab 2D: Node Embeddings & Edge Scoring

This simulator builds a random community-structured graph, gives every node a random 2D embedding, and hides a fraction of the real edges as a test set. A GCN-style neighbor-mean aggregation step — the same mechanism behind GCN, GraphSAGE and GAT-style message passing — repeatedly pulls each node's embedding toward the average of its visible (train) neighbors on a pannable, zoomable plane. Score every non-train pair by cosine similarity or raw dot product, sweep a threshold to see which held-out links get correctly predicted versus missed, watch false-positive predictions light up, and track a live AUC estimate as the embeddings converge.